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main/api.py
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671
main/api.py
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from typing import Union
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from fastapi import FastAPI,Query,UploadFile,File,HTTPException,Request,Path,Form
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from fastapi.responses import StreamingResponse,FileResponse,RedirectResponse
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel,Field
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from typing import Optional,List,Dict,Any
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import json
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import time
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import datetime
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import json5
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import shutil
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############################ 模块代码 ########################
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#全文检索
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from full_index_search import full_search,doc_search,c_or_u_baike_index,c_or_u_fs_index,one_doc_search,delete_index_by_docid,adjust_ctx_size,full_search_by_baike_catalog,full_search_by_doc_list
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#初始化信息
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from init import *
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from utils import regular_filename
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#文件仓库
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from ff import sftp_list_directory,smb_list_directory,linux_list_directory,ftp_list_directory,create_doc_cache
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#数据库
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from k_database import QA,BaiKe,BaiKe_Catalog,Doc,KAgent
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#
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from peewee import fn
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from agent_chat import Agent_chat_build
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#得到客户端ip
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def get_client_ip(request):
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x_forwarded_for = request.headers.get("X-Forwarded-For")
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if x_forwarded_for:
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client_ip = x_forwarded_for.split(",")[0]
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else:
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# 如果没有 X-Forwarded-For，尝试从 X-Real-IP 获取
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client_ip = request.headers.get("X-Real-IP")
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# 如果以上都没有，使用 request.client.host
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if not client_ip:
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client_ip = request.client.host
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return client_ip
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#KAgent的sn查询,对外使用
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#@app.get("/api/kagent_cfg/{kasn}")
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def get_kagent_by_sn(kasn: str,request:Request):
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if kasn=='superman':
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title,demo,atype,guide,files = "超级个人助理","AI革命数字永生",3,"你好！我是你的超级个人助手！我可以调用我的发布和我的订阅智能体来帮你完成任务规划和执行，有事不决请问我！","S"
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else:
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#根据sn获得知识体的详细信息
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try:
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query = KAgent.select(KAgent.title,KAgent.demo,KAgent.atype,KAgent.guide,KAgent.files).where(KAgent.kasn==kasn)
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title,demo,atype,guide,files = query.scalar(as_tuple=True)
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except:
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return {"title":"未知应用","guide":"未能正确识别SN",'atype':"","files":"","demo":"SN不正确,无法访问应用"}
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username = request.cookies.get("username", "匿名用户")
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username = decode_username(username)
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cfg = {"title":title,"guide":guide,'atype':atype,"files":files,"demo":demo,"username":username}
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#钉钉参数
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if gcfg["dingtalk"]["app_key"]!="":
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dingtalk={"client_id":gcfg["dingtalk"]["app_key"],
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"close_login":gcfg["dingtalk"]["exclusiveLogin"],
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"corpid":gcfg["dingtalk"]["exclusiveCorpId"],
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}
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cfg["dingtalk"]=dingtalk
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#微信参数
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if gcfg["wechat"]["app_key"]!="":
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cfg["wechat"] = gcfg["wechat"]["app_key"]
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#logo
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cfg["logo"] = gcfg["fs"]["logo"]
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#官网
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cfg["url"]=gcfg["fs"]["url"]
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#服务码
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cfg["aicode"]=gcfg["fs"]["aicode"]
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#分类:
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cfg["agent_catalogs"] = get_gcfg_mem()["agent_catalogs"]
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return cfg
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#KAgent对话
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#写入对话上下文
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def write_chat_context(chat_id,context):
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with open(f"/tmp/k3gpt/{chat_id}","w+") as f:
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json.dump(context,f,ensure_ascii=False)
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#删除对话上下文
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def delete_chat_by_id(chat_id):
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try:
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os.remove(f"/tmp/k3gpt/{chat_id}")
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except:
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pass
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#获取上下文
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def get_chat_context(chat_id):
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try:
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with open(f"/tmp/k3gpt/{chat_id}","r") as f:
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context = json.load(f)
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return context
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except Exception as e:
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raise Exception(f"上下问读取失败:{e}")
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from typing import Optional
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class Chat(BaseModel):
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question: str = Field(..., example="你是谁？")
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ai_mode: Optional[str] = Field("", example="对话模式")
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base:Optional[str] = Field(None, example="知识库")
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file: Optional[str] = Field(None, example="文件名")
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path: Optional[str] = Field(None, example="路径")
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history: Optional[list] = Field(None, example="历史对话")
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kagent_sn: Optional[str] = Field(None, example="Agent的sn,识别码")
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mem: Optional[list] = Field([], example="可选的短期记忆")
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#KAgent对话
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def chat_kagent(chat:Chat):
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chat_id=f"chat_{time.time()}"
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count=0
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ctx=[] #上下文信息
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#根据sn获得知识体的详细信息
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if chat.kagent_sn=='superman':
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title,atype,prompt,files = "超级个人助理",3,"# 你是一个超级个人助理,优先使用100来回答问题,回答不好的再使用工具和其它智能体,你可以根据成功案例和失败案例进行参考","S"
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else:
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query = KAgent.select(KAgent.title,KAgent.atype,KAgent.prompt,KAgent.files).where(KAgent.kasn==chat.kagent_sn)
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title,atype,prompt,files = query.scalar(as_tuple=True)
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#上传文件
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upload_files=[]
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if chat.base and chat.path:
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upload_files.append([chat.base,chat.path])
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if not files:
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files=""
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#根据类型进行相应的处理
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if atype in [0,1]:
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doc_list=[]
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#全文检索相关知识
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if atype==0:
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doc_list = files.split(",")
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elif atype==1: #关键字检索文档列表
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#支持多个目录
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from functools import reduce
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from operator import or_
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conditions =[]
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for file in files.split(","):
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conditions.append(Doc.abs_path.contains(file))
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# 使用 reduce 将多个条件用 OR 合并
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combined_condition = reduce(or_, conditions)
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query= (Doc
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.select(Doc.base,Doc.abs_path)
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.where( combined_condition )
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.order_by(Doc.f_up_at.desc())
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.limit(10000)
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)
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for r in query:
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doc_list.append(f"{r.base}_{r.abs_path}")
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#end if
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print("相关文档数量",len(doc_list))
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#搜索相关的百科
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context0 = []
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#确认是否有满足问题的百科数据
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context0 = full_search_by_baike_catalog(chat.question,f"智能体数据/{title}")
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#搜索文件
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context = full_search_by_doc_list(chat.question,doc_list)
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if len(context0)==0: #没有百科相关信息
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context = adjust_ctx_size(context)
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count = len(context)
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if chat.ai_mode.find("Detailed")==0:
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for i in range(count):
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ctx.append({"name":i,"path":i,"base":i,"ctx":context[i],"size":len(context[i])})
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write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":context,"mem":chat.mem,
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"prompt":prompt,"history":chat.history,"upload_files":upload_files})
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elif len(context0) < 10 or len(context) < 10:
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#百科信息比较少的情况，合并知识百科的内容，进行回答
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context0.extend(context)
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context = adjust_ctx_size(context0)
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count = len(context)
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if chat.ai_mode.find("Detailed")==0:
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for i in range(count):
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ctx.append({"name":i,"path":i,"base":i,"ctx":context[i],"size":len(context[i])})
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write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":context,"mem":chat.mem,
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"prompt":prompt,"history":chat.history,"upload_files":upload_files})
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else:
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#百科的相关内容很多
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#依赖知识百科，二次回答，时间比较长
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#第一步根据检索信息，得到大模型回答的结果
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context = adjust_ctx_size(context)
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count = len(context)
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answer = do_chat_agent_nostream({"title":title,"req":chat.question,"atype":atype,"ctx":context,"mem":chat.mem,
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"prompt":prompt,"history":chat.history,"upload_files":upload_files})
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#第二步，结合结合知识百科的信息，进行综合回答
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context = adjust_ctx_size(context0)
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context.insert(0,f"搜索答案: {answer}")
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count = len(context)
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prompt="""
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# 智能助手
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你是一个问答机器人的助手,请使用提供的上下问来回答问题.
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# 回答逻辑
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1. 上下文中标有知识百科的数据可信度很高，可以优先采纳
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2. 搜索答案是通过大模型RAG得到的答案
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3. 如果知识百科的数据和问题关联性很高时，可以讲百科的数据和搜索答案进行融合来回答
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4. 如果知识百科的数据和问题关联性不高时，可以直接使用搜索答案来回答
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# 上下文
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{context}
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# 问题
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{question}
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"""
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if chat.ai_mode.find("Detailed")==0:
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for i in range(count):
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ctx.append({"name":i,"path":i,"base":i,"ctx":context[i],"size":len(context[i])})
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write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":context,
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"prompt":prompt,"history":chat.history,"upload_files":upload_files,"mem":chat.mem})
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else:
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#非检索的、融合检索的、上传文件的
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if chat.mem:
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ctx.append(chat.mem)
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write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":ctx,
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"prompt":prompt,"ai_mode":chat.ai_mode,
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"history":chat.history,"fun_def":files,"upload_files":upload_files})
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return {"chat_id":chat_id,"count":count,"ctx":ctx}
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def decode_username(username):
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if username !="匿名用户":
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# 原始字符串
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#raw_string = "b'\\xe9\\x83\\x9c'"
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# 提取字节部分
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byte_sequence = username[2:-1] # 去掉前后的 `b'` 和 `'`
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# 将转义的字节字符串转换为实际的字节对象
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byte_string = bytes(byte_sequence, 'utf-8').decode('unicode_escape').encode('latin1')
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# 解码为正常字符串
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username = byte_string.decode('utf-8')
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return username
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#KAgent对话的流输出，采用事件流机制，实时输出
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#@app.get("/api/chat_kagent/{chat_id}")
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def chat_event(chat_id: str,request:Request):
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userid = request.cookies.get("userid", "id-00001")
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if userid=="id-00001":
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return StreamingResponse(sse_do_response(do_chat_error(chat_id),chat_id,
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"k3gpt"),media_type="text/event-stream")
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username = request.cookies.get("username", "匿名用户")
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username = decode_username(username)
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client_ip = get_client_ip(request)
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return StreamingResponse(
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sse_do_response(
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do_chat_agent(chat_id,userid,username,client_ip),
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chat_id,
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"k3gpt"),
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media_type="text/event-stream")
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def do_chat_error(chat_id):
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delete_chat_by_id(chat_id)
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yield "用户认证信息丢失，请重新认证后再访问！"
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#SSE+不同的返回format()
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def sse_do_response(gen,chat_id,format="k3gpt"):
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if format=="k3gpt":
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for content in gen:
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if content.endswith("<new>") and len(content)>5:
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yield gen_k3gpt_result(content[0:-5])
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yield gen_k3gpt_result("<new>")
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elif content.startswith("<tool>"):#工具+数据
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data_start = content.find("<data>")
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yield gen_k3gpt_result2(content[6:data_start],content[data_start+6:])
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else:
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yield gen_k3gpt_result(content)
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else:
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for content in gen:
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if content.endswith("<new>") and len(content)>5:
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yield gen_openai_result(chat_id,content[0:-5])
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else:
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yield gen_openai_result(chat_id,content)
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#生成类OpenAI的流式diego结果
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def gen_openai_result(chat_id,data):
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messages=[]
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messages.append({"delta":{"role":"assistant","content":data}})
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response={"id":chat_id,"object":"chat","choices":messages}
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json_str = "data: " + json.dumps(response) + "\n\n"
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return json_str.encode("utf-8")
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#返回结果信息和UI保持一致，rsp为真正内容
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def gen_k3gpt_result(data):
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json_data={}
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json_data["rsp"] = data
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json_str = "data: " + json.dumps(json_data) + "\n\n"
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return json_str.encode("utf-8")
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#返回结果信息和UI保持一致，rsp为真正内容,tool_name为工具名称,用来处理chart和表格的json数据
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def gen_k3gpt_result2(tool_name,data):
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json_data={}
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json_data["rsp"] = data
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json_data["type"] = tool_name
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json_str = "data: " + json.dumps(json_data) + "\n\n"
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return json_str.encode("utf-8")
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def found_llm_think_data(data):
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think=""
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if data.find("<think>")>=0 and data.find("</think>")==-1:
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#还未思考结束
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think = data[8:]
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data = ""
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elif data.find("<think>")>=0 and data.find("</think>")>0:
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#思考结束
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end = data.find("</think>")
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think = data[8:end]
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data = data[end+8:]
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if data.find("```json") >=0:
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#找到json数据，只返回json数据
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begin = data.find("{")
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end = data.rfind("}")
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data = data[begin:end+1]
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return think,data
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#KAgent chat的主函数
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def do_chat_agent(chat_id,userid="10000",username="Admin",client_ip="127.0.0.1",delta_stream=False):
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chat = get_chat_context(chat_id)
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chat["chat_id"]=chat_id
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title = chat["title"]
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question = chat["req"]
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atype = chat["atype"] #智能体类型
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prompt = chat["prompt"]
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history = chat["history"]
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context = chat["ctx"]
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answer = ""
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if atype==3:
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#融合智能检索模式
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agent_chat = Agent_chat_build(chat,delta_stream,username,userid)
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answer = yield from agent_chat.do_chat_loop()
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else:
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if "mem" in chat:
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mem = chat["mem"]
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else:
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mem = []
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# 获取当前日期和星期
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today = datetime.datetime.now()
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# 获取今天是星期几，使用weekday()方法（周一=0, 周日=6）
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weekday_number = today.weekday()
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now=f"{today.strftime('%Y-%m-%d %H:%M:%S')} 星期{weekday_number+1}"
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#用户不用再添加部分内容
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if prompt.find("{context}")==-1 or prompt.find("{question}")==-1:
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prompt +="""
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### 当前时间
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{now}
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### 问题
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{question}
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### 上下文
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{context}
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"""
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upload_files = chat["upload_files"]
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file_content=""
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if upload_files:
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print("上传文件",upload_files)
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#只取第一个文件
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f1 = [f'{upload_files[0][0]}_{upload_files[0][1]}']
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||||
filename = upload_files[0][1]
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file_contents = full_search_by_doc_list("",f1)
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file_prompt = prompt +"""
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||||
### 文件
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||||
{filename}
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||||
### 文件内容
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||||
{file_content}
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||||
"""
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||||
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||||
if len(file_contents)>1:
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||||
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||||
answers = []
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||||
for i,file_content in enumerate(file_contents):
|
||||
print(f"分片[{i}], 大小: {len(file_content)}")
|
||||
system_prompt = file_prompt.format(**{"query":question,"question":question,"context":context,"now":now,"file_content":file_content,"filename":filename})
|
||||
answer = yield from llm_ask(system_prompt,delta_stream)
|
||||
answers.append(answer)
|
||||
|
||||
#汇总答案
|
||||
sum_prompt = prompt +"""
|
||||
# 上下文是文件的多个分片分别计算的到的结果，现在合并上下问，结合用户的问题，做最后的回答输出
|
||||
"""
|
||||
system_prompt = sum_prompt.format(**{"query":question,"question":question,"context":answers,"now":now,"history":history})
|
||||
sum_answers = yield from llm_ask(system_prompt,delta_stream)
|
||||
answer = answers
|
||||
|
||||
elif len(file_contents)==1:
|
||||
file_content = file_contents[0]
|
||||
system_prompt = file_prompt.format(**{"query":question,"question":question,"context":context,"now":now,"history":history,"file_content":file_content,"filename":filename})
|
||||
answer = yield from llm_ask(system_prompt,delta_stream)
|
||||
else:
|
||||
yield f"没有找到文件的数据分片:{filename}"
|
||||
else:
|
||||
prompt +="""
|
||||
### 上下文2
|
||||
{mem}
|
||||
"""
|
||||
system_prompt = prompt.format(**{"query":question,"question":question,"context":context,"now":now,"history":history,"mem":mem})
|
||||
answer = yield from llm_ask(system_prompt,delta_stream)
|
||||
|
||||
#删除文件
|
||||
QA.create(userid=userid,username=username, question=question,answer=answer,ref=f"知识体:{title}",client_ip=client_ip)
|
||||
delete_chat_by_id(chat_id)
|
||||
return answer
|
||||
|
||||
#大模型回答,
|
||||
def llm_ask(system_prompt,delta_stream):
|
||||
messages = [{'role': 'user', 'content': system_prompt}]
|
||||
responses = llm.chat(
|
||||
messages=messages,
|
||||
stream=True,
|
||||
delta_stream=delta_stream
|
||||
) # get a new response from the model where it can see the function response
|
||||
|
||||
answer=""
|
||||
try:
|
||||
|
||||
for response in responses:
|
||||
#print(response[0]["content"])
|
||||
yield response[0]["content"]
|
||||
if delta_stream==True:
|
||||
answer +=response[0]["content"]
|
||||
if not delta_stream:
|
||||
think,data = found_llm_think_data(response[0]["content"])
|
||||
answer = data
|
||||
except Exception as e:
|
||||
yield "大模型运行出现错误:{e}, 请检查配置是否正确或者网络是否通畅!\n如果是本地大模型还请检查大模型是否正常启动!"
|
||||
return answer
|
||||
|
||||
|
||||
#KAgent chat内部使用函数，非流输出
|
||||
def do_chat_agent_nostream(chat):
|
||||
question = chat["req"]
|
||||
mode = chat["mode"]
|
||||
prompt = chat["prompt"]
|
||||
history = chat["history"]
|
||||
context = chat["ctx"]
|
||||
doc_list = chat["doc_list"]
|
||||
|
||||
# 获取当前日期和星期
|
||||
today = datetime.datetime.now()
|
||||
# 获取今天是星期几，使用weekday()方法（周一=0, 周日=6）
|
||||
weekday_number = today.weekday()
|
||||
|
||||
now=f"{today.strftime('%Y-%m-%d %H:%M:%S')} 星期{weekday_number+1}"
|
||||
|
||||
system_prompt = prompt.format(**{"query":question,"question":question,"context":context,"now":now,"history":history,"doc_list":doc_list})
|
||||
|
||||
messages = [{'role': 'user', 'content': system_prompt}]
|
||||
responses = llm.chat(
|
||||
messages=messages,
|
||||
stream=False,
|
||||
) # get a new response from the model where it can see the function response
|
||||
|
||||
return responses[0]["content"]
|
||||
|
||||
|
||||
#文件中心上传文件
|
||||
#@app.post("/api/upload")
|
||||
async def upload_file(request:Request,curpath:str=Form(''), file: UploadFile = File(...)):
|
||||
try:
|
||||
username = request.cookies.get("username", "匿名用户")
|
||||
username = decode_username(username)
|
||||
#上传文件空间
|
||||
if not os.path.exists(f'{gcfg["fs"]["path"]}/个人文件夹/{username}'):
|
||||
os.makedirs(f'{gcfg["fs"]["path"]}/个人文件夹/{username}')
|
||||
|
||||
#去除空格
|
||||
filename = regular_filename(file.filename)
|
||||
|
||||
with open(f"{gcfg['fs']['path']}/个人文件夹/{username}/{filename}", "wb") as buf:
|
||||
shutil.copyfileobj(file.file, buf)
|
||||
fsize = buf.tell()
|
||||
except Exception as e:
|
||||
return {"errno":1,"message":f"上传出错: {e}"}
|
||||
|
||||
#建立索引和文件记录
|
||||
c_or_u_fs_index(filename,f"{gcfg['fs']['path']}/个人文件夹/{username}/{filename}",fsize,username)
|
||||
|
||||
|
||||
return {"errno":0,"file":{"name":filename,"path":f"{gcfg['fs']['path']}/个人文件夹/{username}/{filename}","base":"文件中心"}}
|
||||
|
||||
#对外提供的大模型对外服务
|
||||
def chat_completions(chat_req:dict,req:Request):
|
||||
#先认证
|
||||
auth_header = req.headers.get("Authorization")
|
||||
if not auth_header:
|
||||
raise HTTPException(status_code=401, detail="Authorization header missing")
|
||||
|
||||
# 示例：Bearer token
|
||||
if not auth_header.startswith("Bearer "):
|
||||
raise HTTPException(status_code=401, detail="Invalid authorization scheme")
|
||||
|
||||
token = auth_header.split(" ")[1] # 提取 token
|
||||
try:
|
||||
#print(token)
|
||||
import base64
|
||||
sn,username_base64 = token.split(":")
|
||||
username = base64.b64decode(username_base64.encode("utf-8")).decode("utf-8")
|
||||
except:
|
||||
raise HTTPException(status_code=401, detail="api_key is error")
|
||||
|
||||
#再内容
|
||||
#chat_req = await req.json()
|
||||
print(chat_req)
|
||||
if 'messages' not in chat_req or not chat_req['messages']:
|
||||
raise HTTPException(status_code=501, detail="message format error")
|
||||
|
||||
question = ""
|
||||
for msg in chat_req['messages']:
|
||||
if msg["role"]=="user":
|
||||
if isinstance(msg["content"],list): #列表类型
|
||||
for content in msg["content"]:
|
||||
if content["type"]=="text":
|
||||
question += content["text"]
|
||||
elif isinstance(msg["content"],str):
|
||||
question += msg["content"]
|
||||
elif msg["role"]=="system":
|
||||
if isinstance(msg["content"],list):#列表类型
|
||||
for content in msg["content"]:
|
||||
if content["type"]=="text":
|
||||
question = content["text"]
|
||||
elif isinstance(msg["content"],str):
|
||||
question = msg["content"]
|
||||
|
||||
chat = Chat(kagent_sn=sn,question=question)
|
||||
chat_info = chat_kagent(chat)
|
||||
|
||||
userid = "50000"
|
||||
client_ip = get_client_ip(req)
|
||||
chat_id = chat_info["chat_id"]
|
||||
if "stream" in chat_req and chat_req["stream"]: #流式输出
|
||||
return StreamingResponse(sse_do_response(
|
||||
do_chat_agent(chat_id,userid,username,client_ip,True),
|
||||
chat_id,
|
||||
"openai"), media_type="text/event-stream")
|
||||
|
||||
#非流式输出，取得最后的结果
|
||||
try:
|
||||
anwser = ""
|
||||
gen = do_chat_agent(chat_id,userid,username,client_ip,True)
|
||||
while True:
|
||||
next(gen)
|
||||
except StopIteration as e:
|
||||
anwser = e.value
|
||||
messages=[]
|
||||
messages.append({"message":{"role":"assistant","content":anwser}})
|
||||
response={"id":chat_id,"object":"chat","choices":messages}
|
||||
print(response)
|
||||
return response
|
||||
|
||||
|
||||
#当前用户的名字和IP
|
||||
def get_user_info(req):
|
||||
# 从请求中获取 Cookie
|
||||
userid = req.cookies.get("userid", "id-00001")
|
||||
username = req.cookies.get("username", "匿名用户")
|
||||
username = decode_username(username)
|
||||
client_ip = get_client_ip(req)
|
||||
return username,client_ip
|
||||
|
||||
|
||||
#百科的保存
|
||||
class New_BaiKe(BaseModel):
|
||||
id: int = Field(None, example="你好，世界！")
|
||||
title: str = Field(..., example="你好，世界！")
|
||||
catalog: str = Field(..., example="你好，世界！")
|
||||
html: str = Field(..., example="你好，世界！")
|
||||
style: Optional[Dict[str, Any]] = None #生成样式
|
||||
trans: Optional[bool] = True
|
||||
|
||||
|
||||
|
||||
#百科的保存
|
||||
#@app.post("/api/baike")
|
||||
def new_baike(baike:New_BaiKe,req:Request):
|
||||
|
||||
username,client = get_user_info(req)
|
||||
|
||||
#增加个人空间的知识百科
|
||||
if baike.catalog=="个人空间":
|
||||
baike.catalog = f"个人空间/{username}"
|
||||
|
||||
if baike.trans:
|
||||
from parse_html import trans_html_to_format
|
||||
#print(baike.html)
|
||||
#print("============================================")
|
||||
baike.html = trans_html_to_format(baike.html)
|
||||
|
||||
if baike.id and baike.id !=0:
|
||||
|
||||
#更新
|
||||
# query = BaiKe.select(BaiKe.full_id).where(BaiKe.id == baike.id)
|
||||
# old_full_id = query.scalar()
|
||||
|
||||
|
||||
# baike_u = BaiKe(id=baike.id)
|
||||
|
||||
# try:
|
||||
# full_id = c_or_u_baike_index(baike.html,baike.title,baike.id,baike_u.full_id,baike.catalog)
|
||||
# except:
|
||||
# full_id = baike_u.full_id
|
||||
|
||||
# baike_u.html = baike.html
|
||||
# baike_u.title = baike.title
|
||||
# baike_u.catalog = baike.catalog
|
||||
# baike_u.m_time = datetime.datetime.now()
|
||||
# baike_u.full_id = full_id
|
||||
# baike_u.save(only=[BaiKe.html,BaiKe.title,BaiKe.catalog,BaiKe.m_time,BaiKe.full_id])
|
||||
|
||||
#这个可以和上面的效果一样
|
||||
c_or_u_baike_index(baike.html,baike.title,baike.id,baike.catalog)
|
||||
BaiKe.update(title=baike.title,catalog=baike.catalog,html=baike.html,m_time=datetime.datetime.now(),modifier=username).where(BaiKe.id == baike.id).execute()
|
||||
|
||||
else:
|
||||
#新建
|
||||
baike=BaiKe.create(title=baike.title,catalog=baike.catalog,html=baike.html,creator=username,modifier=username)
|
||||
|
||||
c_or_u_baike_index(baike.html,baike.title,baike.id,baike.catalog)
|
||||
|
||||
#Operate_Log(req,"新建",f'[百科] id:{baike.id},标题:{baike.title}')
|
||||
|
||||
# baike.full_id = full_id
|
||||
# baike.save(only=[BaiKe.full_id])
|
||||
|
||||
|
||||
return {"msg":"OK","id":baike.id}
|
||||
Reference in New Issue
Block a user